What is the best voice to text software?

Voice to text software, also known as speech recognition or speech-to-text software, allows users to dictate speech which is then transcribed into text. This technology has many useful applications, from productivity to accessibility. Voice to text makes it possible to draft documents, send messages, search the web, and more using only your voice.

The foundations of modern voice recognition technology were developed in the 1950s and 1960s, but it wasn’t until the 1990s that the first commercial speech recognition software programs emerged. With advancements in deep learning and AI, today’s voice to text capabilities are incredibly accurate and can understand natural language.

When evaluating voice to text software, some key criteria include accuracy, integration with applications, customization options, speed, ability to understand multiple speakers, availability of cloud vs offline functionality, supported languages, and pricing.

Accuracy

Accuracy is crucial when it comes to voice transcription software. Even small error rates can lead to major inaccuracies and miscommunications in the final transcripts. According to benchmarks published in 2021, top voice transcription services still have error rates ranging from 5-20% (source).

Google’s speech recognition technology is considered one of the most accurate, with a word error rate around 5% as of 2022 (source). Microsoft and Amazon’s services have slightly higher error rates, averaging around 10-15%. Smaller companies and open source speech recognition projects tend to have even higher error rates above 15%.

While no service is perfect, opting for an industry leader like Google Speech-to-Text will provide the highest accuracy currently available. However, accuracy continues improving across services as speech recognition technology and machine learning advance.

Integration

One of the key factors when evaluating voice-to-text software is how well it integrates with popular apps and services like Slack, Zoom, Google Docs, Microsoft Office and more. The top options provide plugins, add-ons or API access to enable tight integration.

For example, Deepgram offers an API that can be easily integrated into any application. AssemblyAI also provides direct integrations with tools like Slack, Zoom, and Google Docs in addition to API access.

Meanwhile, Microsoft Azure Speech integrates seamlessly into Microsoft apps like Word and PowerPoint, while Google Cloud Speech is designed specifically for Google Workspace apps like Docs and Slides. Amazon Transcribe can integrate with many AWS services.

When evaluating options, look for breadth of integrations, how easy they are to set up, and how tightly integrated they are for a smooth user experience. Tight, native integrations that don’t require switching between apps generally provide the best experience.

Customization

One of the most important features of voice to text software is the ability to customize the vocabulary and command sets. This allows the software to accurately transcribe industry-specific jargon, names, and unique phrases used by the individual user. Having robust customization options improves transcription accuracy tremendously.

Some of the top voice to text software options allow users to add custom words and phrases to the program’s vocabulary. For example, Google Docs Voice Typing enables users to add new words and names that it will recognize going forward. Other programs like Dragon Professional take customization even further by letting users train the software on how they speak, their accents, voice tone and pacing. This results in highly personalized transcription tailored to the individual.

In summary, customization gives users control over the vocabulary and commands, improving accuracy and efficiency especially for unique terminology. The best voice to text programs allow easy addition of custom words and phrases, as well as user training for personalized recognition.

Speed

The speed of a voice to text software is crucial for many users. The ability to quickly transcribe audio in real-time or close to real-time allows for efficient documentation, communication, and productivity. For example, journalists conducting interviews rely on fast transcription to capture quotes and details. Likewise, professionals in meetings or giving presentations benefit from rapid voice-to-text abilities to record notes and key insights.

According to Google Cloud Speech-to-Text, Google’s API processes audio faster than real-time, transcribing 30 seconds of audio in just 15 seconds on average. Meanwhile, a Popular Mechanics study found speech dictation to be 3 times faster than typing in English and 2.8 times faster in Mandarin. The top voice to text software can reach speeds over 200 words per minute with very high accuracy.

When evaluating speed, it’s also important to consider factors like latency/delay, ability to keep up with natural talking pace, and performance with long recordings. The fastest options utilize cloud computing and AI to deliver real-time results with minimal lag.

Multiple Speakers

The ability to handle multiple speakers is crucial for accurately transcribing meetings, interviews, focus groups, and other multi-person audio recordings. Situations where multiple speaker support is needed include:

Transcribing business meetings with multiple attendees participating

Converting recordings of group interviews or panel discussions into text
Creating transcripts from qualitative research recordings with multiple focus group participants
Transcribing audio from videos or podcasts with interviews featuring multiple guests

Some voice to text software can distinguish between different speakers and label each one separately in the transcript. This allows you to see who said what in the conversation. According to Google Cloud, their Speech-to-Text API attempts to distinguish the different voices included in the audio sample (https://cloud.google.com/speech-to-text/docs/multiple-voices).

However, not all voice to text services offer robust multiple speaker identification. Testing from TranscribeMe showed the best accuracy for handling multiple speakers came from TranscribeMe, Trint, and Temi – with over 90% accuracy for 2+ speakers. Other options like Otter.ai and Google Cloud Speech-to-Text struggled more with accurately separating speakers (https://transkriptor.com/best-transcription-software-for-multiple-speakers/).

Cloud vs. Offline Speech-to-Text

Cloud-based speech-to-text services offer several advantages compared to offline software installed on a device:

Higher accuracy – Cloud services leverage large datasets and continual model improvements for better transcription accuracy.
Support for multiple languages – Cloud APIs like Google Cloud Speech-to-Text support over 125 languages and variants.
Easy scaling – Cloud services can handle high volumes of audio without needing to provision infrastructure.

Accessibility – Users can access cloud services from any device with an internet connection.

However, offline software has some benefits too:

Privacy – Keeping data processing on device avoids transmitting sensitive audio to the cloud.

Lower latency – No roundtrip to servers means faster results.
Works offline – Still functions without an internet connection.

For most use cases, a cloud API like Microsoft Azure Speech-to-Text is recommended for convenience, scalability, and accuracy. But for privacy-sensitive applications or environments with unreliable connectivity, an offline solution like Dragon NaturallySpeaking may be preferable.

Supported Languages

Support for multiple languages is crucial for voice-to-text software to be useful for a global audience. The top options provide extensive language support, with some key differences:

Google Cloud Speech-to-Text supports over 120 languages, including less common languages like Icelandic and Malay. Google Cloud Speech-to-Text V2 supports over 60 languages.

Microsoft Azure Speech Service supports over 45 languages including Chinese, English, French, German, Italian, Japanese, Portuguese, Spanish and more.

Overall, Google Cloud Speech-to-Text has the most extensive language support, followed by Microsoft Azure Speech Service. However, Azure still supports the most widely spoken languages. For most users, the language support of both services will be sufficient.

Having voice-to-text software that works well in your native language is critical. When comparing options, be sure to verify they support the languages you need.

Pricing

Speech to text software is available in a variety of pricing models, from free options to monthly or annual paid subscriptions. For individuals, free software like Google’s Speech to Text API provides limited usage per month. Paid options like Dragon Professional Individual at $300 per license, and Otter.ai’s Personal Plan at $12/month offer more features and unlimited use.

For businesses, Google’s Speech to Text API costs $0.024 per minute of audio processed, while solutions like Nuance Dragon Professional range from $600 to $2400 per license depending on features. Enterprise-level speech recognition with extensive customization and integrations is available through companies like Otter.ai, starting at $20 per user per month. Most business plans allow volume discounts based on the number of users.

When evaluating pricing for speech to text needs, key factors are the volume of audio to transcribe, number of users, required accuracy rates, customization needs, and integration with existing software. Free software works for individual or very light use, while paid solutions provide more robust options for frequent business use.

Conclusions

Based on the core factors of accuracy, integration, customization, speed, multi-speaker support, cloud vs. offline use, supported languages, and pricing, some of the top options for voice-to-text software include Dragon Professional, Otter.ai, Google Docs Voice Typing, and Microsoft Word Dictation. Dragon Professional leads for on-device accuracy, customization, and speed for single users. Otter.ai excels at synchronous transcription for meetings with multiple speakers. Google Docs and Microsoft Word provide free built-in options, but have limitations in accuracy and features.

Some additional factors to keep in mind when selecting voice-to-text software are privacy, data security, technical support, smartphone app availability, industry-specific capabilities, and accessibility features. Consider which factors are most important for your individual or business needs. Testing out free trials of paid software can help inform buying decisions.

In summary, identify your use case, accuracy needs, budget, and platform constraints when choosing a voice-to-text solution. Leading options provide high accuracy, deep integration, robust features, and strong privacy protections to turn speech into editable, shareable text.